Resources
Related courses
- Privacy in Statistics and Machine Learning (video) Spring 2021 by Adam Smith (BU) and Jonathan Ullman (NEU)
- Data Privacy (mostly differential privacy) Fall 2021 by Joe Near (U of Vermont)
- Algorithms for Private Data Analysis (video) Fall 2020 by Gautam Kamath (Waterloo)
- Applied Privacy for Data Science Spring 2019 by James Honaker and Salil Vadhan (Harvard)
- Introduction to Differential Privacy: Theory, Algorithms and Applications Fall 2021 by Yuxiang Wang (UCSB)
- Design of Stable Algorithms for Privacy and Learning Fall 2016 by Ashwin Machanavajjhala (Duke)
- Algorithms for Private Data Analysis Fall 2020 by Aleksandar Nikolov (UofT)
- Data Privacy Spring 2015 by Andrej Bogdanov (CUHK)
- Differential Privacy: From Theory to Practice 2017 Winter school by Katrina Ligett (Hebrew), Kobbi Nissim (Georgetown), Vitaly Shmatikov (Cornell), Adam Smith (BU), Jon Ullman (NEU)
Books
- The Algorithmic Foundations of Differential Privacy
- Differential Privacy: From Theory to Practice
- Programming Differential Privacy
- The Complexity of Differential Privacy
- Differential Privacy: A Primer for a Non-Technical Audience
- Protecting Your Privacy In A Data-driven World
- Differential Privacy for Databases
More courses on other flavors
- Theoretical data analysis: The Algorithmic Foundations of Adaptive Data Analysis Fall 2017 by Aaron Roth (Penn) and Adam Smith (BU)
- System and/or ml: Private Systems Spring 2020 by Roxana Geambasu (Columbia)
- Database systems: Building Privacy-aware Database Systems Spring 2021 by Xi He (Waterloo)
- Mechanism design: Differential Privacy in Game Theory and Mechanism Design Spring 2014 by Aaron Roth (Penn)
- Fairness: Privacy & Fairness In Data Science Fall 2018 by Ashwin Machanavajjhala (Duke)
- ML: Privacy Preserving Machine Learning Spring 2021 by Aurélien Bellet (Inria)